🛒 Amazon E-commerce Analytics Capstone Project

End-to-end project analyzing Amazon E-commerce data using Python, MySQL, and Power BI — uncovering customer behavior, sales trends, and feedback insights.

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⚙️ Project Workflow

1️⃣ Python (Pandas): Data extraction & cleaning (6 relational tables).
2️⃣ MySQL: KPI and analytical insights (sales, revenue, refunds, customer retention).
3️⃣ Power BI: 13-page interactive dashboard for business decision support.

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📊 Key Highlights

👥 Customers: Top 3 cities, most active age group, repeat customer rate, top spenders
📦 Orders: Monthly trends, city-wise revenue, premium order share
🛍️ Products: Top-performing products, most refunded items, average delivery ratings
💳 Transactions: Payment mode share, city-wise preference, refund trends
🔁 Refunds: Common return reasons, highly returned products, monthly return trends
⭐ Feedback: Rating trends, top & lowest-rated products

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🖥️ Power BI Dashboard Snapshots

Below are some of the key pages from my 13-page Power BI report.
Each page focuses on a different business area of Amazon’s E-commerce operations.

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📊 Dashboard Overview

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👥 Customers Report

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📦 Orders Report

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🛍️ Products Report

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💳 Transactions Report

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🔁 Refunds Report

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⭐ Feedbacks Report

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📁 Note: The visuals folder contains all 13 Power BI pages (1 overview + 12 detailed reports). Only the key pages are shown above for preview.